Natural Language Processing (NLP) continues to grow in popularity in a range of research and commercial applications. However, installing, maintaining, and running NLP tools can be time consuming, and many commercial and research end users have only intermittent need for large processing capacity. This paper describes ILLINOISCLOUDNLP, an on-demand framework built around NLPCURATOR and Amazon Web Services' Elastic Compute Cloud (EC2). This framework provides a simple interface to end users via which they can deploy one or more NLPCURATOR instances on EC2, upload plain text documents, specify a set of Text Analytics tools (NLP annotations) to apply, and process and store or download the processed data. It also allows end users to use a model trained on their own data: ILLINOISCLOUDNLP takes care of training, hosting, and applying it to new data just as it does with existing models within NLPCURATOR. As a representative use case, we describe our use of ILLINOISCLOUDNLP to process 3.05 million documents used in the 2012 and 2013 Text Analysis Conference Knowledge Base Population tasks at a relatively deep level of processing, in approximately 20 hours, at an approximate cost of US$500; this is about 20 times faster than doing so on a single server and requires no human supervision and no NLP or Machine Learning expertise.